ADE 1 Practice Test

Disaggregating data by subgroups is the essential first move because it makes invisible inequities visible. When you look only at overall or average results, you can miss large gaps between groups (for example, differences by race, income, or special education status). By breaking the data into subgroups, you can see precisely which groups are underperforming and how large the gaps are. This targeted picture is what lets a district focus resources and tailor strategies to the students who need them most, rather than applying broad programs that may not reach the groups most affected.

For instance, you might find that overall proficiency is decent, but one subgroup trails far behind. That insight guides where to direct tutoring, teacher supports, or changes in instructional approaches to close those specific gaps. After identifying the gaps, you can set concrete, measurable targets for those groups and monitor progress over time.

Relying on averages can mask disparities, and focusing on a single outcome like graduation rate may overlook earlier-year gaps that contribute to that outcome. A data-driven disaggregation first step ensures interventions address the real, lived inequities in the district.